RFCDE: Random Forests for Conditional Density Estimation
This provides a tool for researchers and practitioners needing to model conditional probability distributions, though it is incremental as it adapts existing random forest methods to a new task.
The authors tackled the problem of nonparametric conditional density estimation by introducing the RFCDE package, which extends random forests to handle joint densities for multiple responses, enabling uncertainty propagation and analysis of relationships between responses and covariates.
Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest models optimized for nonparametric conditional density estimation, including joint densities for multiple responses. This enables analysis of conditional probability distributions which is useful for propagating uncertainty and of joint distributions that describe relationships between multiple responses and covariates. RFCDE is released under the MIT open-source license and can be accessed at https://github.com/tpospisi/rfcde . Both R and Python versions, which call a common C++ library, are available.